library(ggplot2)
library(tidyverse)
library(GGally)
library(ggpubr)
#load the dataframes for the figure
remove cells in the edges : pos.x > 45 & < 1150 pos.y >
45 & < 1000 ##add the replicate info
aic.df <- aic.df %>%
mutate(exp.field = paste0(str_split(cell.id, "_", simplify = T)[,2],"_",
str_split(cell.id, "_", simplify = T)[,3]),
colony = case_when(exp.field %in% c("20min_s3", "20min_s4") ~ "Replicate 1",
exp.field %in% c("20min_s5", "20min_s6") ~ "Replicate 2",
exp.field %in% c("20min_s7", "20min_s8" , "20min_s9") ~ "Replicate 3"))
aic.df <- aic.df %>%
mutate(colony = ifelse(degron == "stable" & red == "pup1-rfp",
case_when(exp.field %in% c("20min_s4", "20min_s5") ~ "Replicate 1",
exp.field %in% c("20min_s6", "20min_s7") ~ "Replicate 2",
exp.field %in% c("20min_s8", "20min_s9") ~ "Replicate 3"), colony))
# filter(value < 0.05)
##Cellular attributes
pup1.cell.attr <- read_csv("~/plots/all_data/all_pup1_cell_attr.csv")
Rows: 19396 Columns: 29
── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (4): cell.id, degron, red, treatment
dbl (25): gfp.mean.bg.af.sub.new, gfp.sum.bg.af.sub, area, area.puncta, BB_B, BB_C, Elip_B, Elip_C, no.of.voxels, pos.x, pos.y, spherac...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
###pup1-RFP background
pup1.cell.attr <- pup1.cell.attr %>%
mutate(exp.field = paste0(str_split(cell.id, "_", simplify = T)[,2],"_",
str_split(cell.id, "_", simplify = T)[,3]),
colony = case_when(exp.field %in% c("20min_s3", "20min_s4") ~ "Replicate 1",
exp.field %in% c("20min_s5", "20min_s6") ~ "Replicate 2",
exp.field %in% c("20min_s7", "20min_s8" , "20min_s9") ~ "Replicate 3"))
pup1.cell.attr <- pup1.cell.attr %>%
mutate(colony = ifelse(degron == "stable" & red == "pup1-rfp",
case_when(exp.field %in% c("20min_s4", "20min_s5") ~ "Replicate 1",
exp.field %in% c("20min_s6", "20min_s7") ~ "Replicate 2",
exp.field %in% c("20min_s8", "20min_s9") ~ "Replicate 3"), colony))
pup1.cell.attr %>%
mutate(exp = str_split(exp.field, "_", simplify = T)[,1]) %>% split(.$exp)
$`20min`
$`40min`
$`60min`
NA
Adding the gfp.sum column for the stable GFP experiment
pup1.cell.attr <- read_csv("~/plots/pup1-rfp-gfp-decay/4-28-21-8hrGFP/data/gfp_stable_filtered.csv") %>%
filter(image.no == 1) %>%
dplyr::select(cell.id, gfpSumBgAFsub) %>%
mutate(cell.id = paste0(cell.id,"_","stable_pup1-rfp_none")) %>%
dplyr::rename("gfp.sum.bg.af.sub" = "gfpSumBgAFsub") %>%
left_join(pup1.cell.attr %>%
filter(degron == "stable") %>%
dplyr::select(-gfp.sum.bg.af.sub),., by = "cell.id") %>%
bind_rows(pup1.cell.attr %>%
filter(degron != "stable"),.)
removing multiple entries of the cell because some cell have more
than one,
multiple.pup1 <- pup1.cell.attr %>%
mutate(dapi.mean.bg.sub.puncta = ifelse(is.na(dapi.mean.bg.sub.puncta), 0, dapi.mean.bg.sub.puncta) ,
rfp.mean.bg.sub.puncta = ifelse(is.na(rfp.mean.bg.sub.puncta), 0 , rfp.mean.bg.sub.puncta)) %>%
filter(rfp.mean.bg.sub.puncta > 0) %>%
group_by(cell.id) %>%
tally() %>%
filter(n>1)
pup1.cell.attr<- pup1.cell.attr %>%
group_by(cell.id) %>%
mutate(rfp.mean.bg.sub.puncta = ifelse(cell.id %in% multiple.pup1$cell.id , mean(rfp.mean.bg.sub.puncta), rfp.mean.bg.sub.puncta),
rfp.sum.bg.sub.puncta = ifelse(cell.id %in% multiple.pup1$cell.id, sum(rfp.mean.bg.sub.puncta), rfp.sum.bg.sub.puncta)) %>%
distinct(cell.id, .keep_all = TRUE)
#proteasome inhibition experiments
protInhi.attr <- read_csv("~/plots/all_data/all_mg135_attr.csv") %>%
rename("cell.id" = "unique.trackID") %>%
filter(timepoint == 1) %>%
mutate(cell.id = paste0(cell.id ,"_",degron,"_",red,"_",treatment)) %>%
mutate(exp.field = paste0(str_split(cell.id, "_", simplify = T)[,2],"_",
str_split(cell.id, "_", simplify = T)[,3]),
colony = case_when(exp.field %in% c("20min_s3", "20min_s4") ~ "Replicate 1",
exp.field %in% c("20min_s5", "20min_s6") ~ "Replicate 2",
exp.field %in% c("20min_s7", "20min_s8" , "20min_s9") ~ "Replicate 3"))
multiple.proInh <- protInhi.attr %>%
mutate(dapi.mean.bg.sub.puncta = ifelse(is.na(dapi.mean.bg.sub.puncta), 0, dapi.mean.bg.sub.puncta) ,
rfp.mean.bg.sub.puncta = ifelse(is.na(rfp.mean.bg.sub.puncta), 0 , rfp.mean.bg.sub.puncta)) %>%
filter(rfp.mean.bg.sub.puncta > 0) %>%
group_by(cell.id) %>%
tally() %>%
filter(n>1)
protInhi.attr <- protInhi.attr %>%
group_by(cell.id) %>%
mutate(rfp.mean.bg.sub.puncta = ifelse(cell.id %in% multiple.proInh$cell.id , mean(rfp.mean.bg.sub.puncta), rfp.mean.bg.sub.puncta),
rfp.sum.bg.sub.puncta = ifelse(cell.id %in% multiple.proInh$cell.id, sum(rfp.mean.bg.sub.puncta), rfp.sum.bg.sub.puncta)) %>%
distinct(cell.id, .keep_all = TRUE)
Adding the protein inhibition experiment data
pup1.proInhi.attr <- pup1.cell.attr %>%
bind_rows(.,protInhi.attr %>%
dplyr::select(-time,
-timepoint,
-image.no,
-ln.gfp,
-ln.gfp.dif,
-trackID,
-gfp.intensity.center,
-gfp.int.mean,
-gfp.int.median,
-gfp.int.sum,
-no.of.triangles,
-field,
-time.dif.gfp,
-real.time.gfp,
-sample,
-avg.gfp.bg,
-Min_gfp,
-gfp.mean.bg.sub,
-value,
-t80,
-t95,
-threshold,
-threshold_95,
-gfp.mean.bg.af.sub,
-threshold_80,
-Mean_gfp,
-image,
-experiment,
-gfp.sum.bg.sub))
#keeping the parameters estimated from the 2-parameter with
maturation model
#only dy.dm
dy.pup1.rep1.mat <- aic.df %>%
filter(red == "pup1-rfp") %>%
filter(ifelse(degron %in% c("stable","stable.2","stable.3"), dy < 0.1, dy < 0.5)) %>%
group_by(cell.id) %>%
filter(model == "dy.dm") %>%
mutate(dm = ifelse(is.na(dm), Inf, dm)) %>%
filter( dm > 0.000015)
#count the number of cells in each experiment
dy.pup1.rep1.mat %>% group_by(degron, treatment) %>% tally()
#creating the df for the MLR
mlr.df <- dy.pup1.rep1.mat %>%
#left join with the df with cellular attributes
left_join(.,pup1.proInhi.attr, by = c("cell.id","red","treatment","degron", "exp.field","colony")) %>%
#remove cells with no rfp puncta info
mutate(rfp.mean.bg.sub.puncta = ifelse(is.na(rfp.mean.bg.sub.puncta), 0, rfp.mean.bg.sub.puncta)) %>%
filter(rfp.mean.bg.sub.puncta > 0)
#checking how many cells there are in each experiment
mlr.df %>% group_by(degron, treatment) %>% tally()
##Removing the columns that I wont regress upon
mlr.df <- mlr.df %>%
filter(pos.x > 45 & pos.x < 1150,
pos.y > 45 & pos.y < 1000) %>%
dplyr::select(-f,
-rfp.mean.af.sub.puncta,
-rfp.sum.af.sub.puncta,
-dapi.sum.af.sub.puncta,
-dapi.mean.af.sub.puncta,
-model,
-k,
-fevals,
-gevals,
-niter,
-convcode,
-kkt1,
-kkt2,
-value,
-xtime,
# -treatment,
-rfp.mean.bg.sub,
-rfp.sum.bg.sub,
-dapi.mean.bg.sub,
-dapi.sum.bg.sub,
-dapi.mean.bg.sub.puncta,
-area.puncta,
-BB_B,
-BB_C,
-pos.x,
-pos.y,
-gfp.sum.bg.af.sub,
-rfp.sum.bg.sub.puncta,
-no.of.voxels,
-volume,
-spheracity) %>%
mutate(shape = Elip_B/Elip_C,
t.half = log(2)/dy) %>%
ungroup() %>%
dplyr::select(-cell.id,
-red,
-exp.field,
-aic,
-Elip_B,
-Elip_C,
-exp)
#Regressors to use for MLR
regressors <- c("dm",
"gfp.mean.bg.af.sub.new",
# "gfp.sum.bg.af.sub",
"area",
# "no.of.voxels",
# "spheracity",
# "volume",
"rfp.mean.bg.sub.puncta",
# "rfp.sum.bg.sub.puncta",
"dapi.sum.bg.sub.puncta",
"shape")
#df where the two experiments of cln2 and stable-GFP are clubbed
mlr.df2 <- mlr.df %>%
filter(degron %in% c("cln2.3","mODC.2","stable.3","stable.2","cln2.4")) %>%
mutate(degron = case_when(degron %in% c("cln2.3","cln2.2","cln2","cln2.4") ~ "yeGFP-CLN2",
degron %in% c("mODC.2", "mODC") ~ "yeGFP-mODC",
degron %in% c("stable","stable.2","stable.3") ~ "yeGFP"))
#df to be used with step function
temp.df.list <- mlr.df2 %>%
filter(treatment == "none") %>%
# mutate( dm = log10(dm)) %>%
split(.$degron)
#using stepAIC from MASS
full.stepAIC.mass %>% map(.,broom::tidy)
$`GFP-CLN2`
$`GFP-mODC`
$yeGFP
NA
#step AIC with all the fp markers + cell shape
full.stepAIC <- lapply(temp.df.list, function(a){
n <- nrow(a)
step(lm(t.half ~ ., data = a %>%
dplyr::select(regressors,
t.half,
-dy,
-treatment)),
direction = "backward",
trace = T,
# k = log(n),
k =2 ) #k = 2 when small dataset and k = log(n) where n = no. of observations for when the dataset is large
})
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(regressors)` instead of `regressors` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.
Start: AIC=5023.53
t.half ~ dm + gfp.mean.bg.af.sub.new + area + rfp.mean.bg.sub.puncta +
dapi.sum.bg.sub.puncta + shape
Df Sum of Sq RSS AIC
- rfp.mean.bg.sub.puncta 1 32.7 36232 5023.0
- shape 1 38.8 36239 5023.3
- area 1 39.3 36239 5023.3
<none> 36200 5023.5
- dm 1 247.6 36447 5032.5
- dapi.sum.bg.sub.puncta 1 918.2 37118 5061.8
- gfp.mean.bg.af.sub.new 1 7805.7 44006 5335.7
Step: AIC=5022.98
t.half ~ dm + gfp.mean.bg.af.sub.new + area + dapi.sum.bg.sub.puncta +
shape
Df Sum of Sq RSS AIC
- shape 1 37.2 36270 5022.6
<none> 36232 5023.0
- area 1 89.4 36322 5024.9
- dm 1 259.8 36492 5032.5
- dapi.sum.bg.sub.puncta 1 970.8 37203 5063.5
- gfp.mean.bg.af.sub.new 1 7803.3 44036 5334.8
Step: AIC=5022.63
t.half ~ dm + gfp.mean.bg.af.sub.new + area + dapi.sum.bg.sub.puncta
Df Sum of Sq RSS AIC
<none> 36270 5022.6
- area 1 82.2 36352 5024.3
- dm 1 249.7 36519 5031.7
- dapi.sum.bg.sub.puncta 1 1048.1 37318 5066.5
- gfp.mean.bg.af.sub.new 1 7848.3 44118 5335.8
Start: AIC=1261.28
t.half ~ dm + gfp.mean.bg.af.sub.new + area + rfp.mean.bg.sub.puncta +
dapi.sum.bg.sub.puncta + shape
Df Sum of Sq RSS AIC
- shape 1 1.67 3519.1 1260.1
- dm 1 2.15 3519.6 1260.3
<none> 3517.5 1261.3
- rfp.mean.bg.sub.puncta 1 5.47 3522.9 1261.8
- area 1 85.84 3603.3 1299.2
- dapi.sum.bg.sub.puncta 1 89.54 3607.0 1300.9
- gfp.mean.bg.af.sub.new 1 479.97 3997.4 1471.2
Step: AIC=1260.06
t.half ~ dm + gfp.mean.bg.af.sub.new + area + rfp.mean.bg.sub.puncta +
dapi.sum.bg.sub.puncta
Df Sum of Sq RSS AIC
- dm 1 2.17 3521.3 1259.1
<none> 3519.1 1260.1
- rfp.mean.bg.sub.puncta 1 5.05 3524.2 1260.4
- area 1 84.64 3603.8 1297.4
- dapi.sum.bg.sub.puncta 1 87.90 3607.0 1298.9
- gfp.mean.bg.af.sub.new 1 482.37 4001.5 1470.9
Step: AIC=1259.08
t.half ~ gfp.mean.bg.af.sub.new + area + rfp.mean.bg.sub.puncta +
dapi.sum.bg.sub.puncta
Df Sum of Sq RSS AIC
<none> 3521.3 1259.1
- rfp.mean.bg.sub.puncta 1 5.13 3526.4 1259.5
- area 1 85.97 3607.3 1297.0
- dapi.sum.bg.sub.puncta 1 87.30 3608.6 1297.7
- gfp.mean.bg.af.sub.new 1 493.27 4014.6 1474.3
Start: AIC=922.36
t.half ~ dm + gfp.mean.bg.af.sub.new + area + rfp.mean.bg.sub.puncta +
dapi.sum.bg.sub.puncta + shape
Df Sum of Sq RSS AIC
<none> 2628.7 922.36
- shape 1 7.60 2636.3 924.18
- area 1 49.53 2678.2 945.06
- rfp.mean.bg.sub.puncta 1 56.77 2685.5 948.63
- dapi.sum.bg.sub.puncta 1 93.01 2721.7 966.37
- dm 1 105.69 2734.4 972.52
- gfp.mean.bg.af.sub.new 1 561.29 3190.0 1176.40
full.stepAIC %>%
map(., broom::glance) %>%
bind_rows(.id = "sample")
# filter(sample %in% c("mODC.2","cln2.3","cln2.4","stable.2","stable.3"))
full.stepAIC %>%
map(., broom::tidy)
$yeGFP
$`yeGFP-CLN2`
$`yeGFP-mODC`
# bind_rows(.id = "sample") %>%
# filter(sample %in% c("mODC.2","cln2.3","cln2.4","stable.2","stable.3"),
# p.value < 0.05) %>%
full.stepAIC %>%
map(.,function(a){
a$anova
}) %>%
bind_rows(.,.id = "degron") %>%
mutate(Step = as.character(Step),
Step = ifelse(Step == "", "Full",Step)) %>%
group_by(degron) %>%
mutate(del.aic = AIC[1]-AIC)
full.stepAIC.fp %>%
map(.,function(a){
a$anova
}) %>%
bind_rows(.,.id = "degron") %>%
mutate(Step = as.character(Step),
Step = ifelse(Step == "", "Full",Step)) %>%
group_by(degron) %>%
mutate(del.aic = AIC[1]-AIC)
NA
NA
#step AIC with only the fluorescent markers
#looking at which regressor contributes to the overall variability
full.stepAIC.fp %>%
map(., broom::tidy)
$`GFP-CLN2`
$`GFP-mODC`
$yeGFP
NA
Variance in t-half explained by each regressor using anova
var.explained
$yeGFP
$`yeGFP-CLN2`
$`yeGFP-mODC`
NA
#plotting the variance explained

#plots:
gfp.6.cor.plt
`geom_smooth()` using formula 'y ~ x'

with protein inhibition
mlr.df %>%
mutate(dm = log10(dm)) %>%
filter(degron %in% c("mODC")) %>%
# filter(!(treatment %in% c("none", "dmso1","50uM")),
# ifelse(treatment == "5uM", t.half < 138.6294, t.half == t.half)) %>%
dplyr::select(t.half,
regressors,
degron,
treatment) %>%
rename(
"GFP" = "gfp.mean.bg.af.sub.new",
"Pup1-tDimer" = "rfp.mean.bg.sub.puncta",
"DNA" = "dapi.sum.bg.sub.puncta",
"Area" = "area"
) %>%
pivot_longer(cols = 2:7) %>%
mutate(degron = case_when(
degron %in% c("mODC") ~ "yeGFP-mODC"),
treatment = factor(treatment, levels = c("none","dmso1","dmso2","1uM","2.5uM","5uM","50uM"))) %>%
filter(!(name %in% c("shape","dm")),
!(treatment %in% c("none","dmso1","50uM")),
t.half < 130) %>%
ggplot(., aes(x = value, y = t.half)) +
ggpointdensity::geom_pointdensity(size = 0.2, guides = F) +
stat_cor(size = 2) +
geom_smooth(method = "lm", se = FALSE, color = "red4", size = 0.5)+
facet_grid(treatment ~ name, scales = "free") +
theme_bw() +
labs(x = NULL) +
theme(text = element_text(size = 8),
axis.text.x = element_text(angle = 30),
legend.key.size = unit(2,"mm"),
legend.position = "none")+
ylab("Half-Life [min.]")+
xlab("A.U")
Warning: Ignoring unknown parameters: guides
`geom_smooth()` using formula 'y ~ x'

#stepAIC with proteasome inhibition experiments With all fp
regressors
temp.df.list.dmso <- mlr.df %>%
filter(treatment != "none", t.half < 130) %>%
split(.$treatment)
full.stepAIC.fp.dmso <- lapply(temp.df.list.dmso, function(a){
step(lm(t.half ~ ., data = a %>%
dplyr::select(regressors.fp,
t.half,
-dy,
-treatment)),
direction = "backward", trace = T)
})
with fp regressors + celluar shape
temp.df.list.dmso <- mlr.df %>%
filter(treatment != "none", t.half < 130) %>%
split(.$treatment)
full.stepAIC.dmso <- lapply(temp.df.list.dmso, function(a){
step(lm(t.half ~ ., data = a %>%
dplyr::select(regressors.fp,
t.half,
-dy,
-treatment)),
direction = "backward", trace = T)
})
Variance in t-half explained by each regressor
full.stepAIC.dmso %>%
map(., anova %>% as.data.frame) %>%
bind_rows(.id = "sample") %>%
rownames_to_column(var = "predictor") %>%
# filter(sample %in% c("mODC.2","cln2.3","cln2.4","stable.2","stable.3")) %>%
group_by(sample) %>%
mutate(var.exp = (value.Sum.Sq/sum(value.Sum.Sq))*100) %>%
split(.$sample)
full.stepAIC.fp.dmso %>%
map(., anova %>% as.data.frame) %>%
bind_rows(.id = "sample") %>%
rownames_to_column(var = "predictor") %>%
# filter(sample %in% c("mODC.2","cln2.3","cln2.4","stable.2","stable.3")) %>%
group_by(sample) %>%
mutate(var.exp = (value.Sum.Sq/sum(value.Sum.Sq))*100) %>%
split(.$sample)
full.stepAIC.dmso %>%
map(., anova %>% as.data.frame) %>%
bind_rows(.id = "sample") %>%
rownames_to_column(var = "predictor") %>%
# filter(sample %in% c("mODC.2","cln2.3","cln2.4","stable.2","stable.3")) %>%
group_by(sample) %>%
mutate(var.exp = (value.Sum.Sq/sum(value.Sum.Sq))*100,
predictor = str_split(predictor, pattern = "\\.\\.\\.", simplify = T)[,1],
predictor = case_when(predictor == "gfp.mean.bg.af.sub.new" ~ "GFP" ,
predictor == "area" ~ "Area",
predictor == "rfp.mean.bg.sub.puncta" ~ "Pup1",
predictor == "dapi.sum.bg.sub.puncta" ~ "DNA" ,
predictor == "shape" ~ "Shape",
predictor == "dm" ~ "Rate of Maturation"
,
TRUE~predictor)) %>%
split(.$sample)
$`1uM`
$`2.5uM`
$`50uM`
$`5uM`
$dmso1
$dmso2
NA
mlr.df %>%
mutate(dm = log10(dm)) %>%
filter(degron %in% c ("mODC") ) %>%
filter(!(treatment %in% c("none", "dmso1","50uM")),
ifelse(treatment == "5uM", t.half < 138.6294, t.half == t.half)) %>%
dplyr::select(t.half,
regressors,
degron,
treatment) %>%
ggplot(.,aes(x = area, y = rfp.mean.bg.sub.puncta))+
geom_point()+
geom_smooth(method = "lm", se = F)+
facet_wrap(~treatment)+
stat_cor()
#ggpairs
gfp.area.cor.ggPair
$`yeGFP-mODC`
plot: [1,1] [====>----------------------------------------------------------------------------------] 6% est: 0s
plot: [1,2] [==========>----------------------------------------------------------------------------] 12% est: 1s
plot: [1,3] [===============>-----------------------------------------------------------------------] 19% est: 1s
plot: [1,4] [=====================>-----------------------------------------------------------------] 25% est: 1s
plot: [2,1] [==========================>------------------------------------------------------------] 31% est: 1s `geom_smooth()` using formula 'y ~ x'
plot: [2,2] [================================>------------------------------------------------------] 38% est: 1s
plot: [2,3] [=====================================>-------------------------------------------------] 44% est: 1s
plot: [2,4] [===========================================>-------------------------------------------] 50% est: 1s
plot: [3,1] [================================================>--------------------------------------] 56% est: 1s `geom_smooth()` using formula 'y ~ x'
plot: [3,2] [=====================================================>---------------------------------] 62% est: 1s `geom_smooth()` using formula 'y ~ x'
plot: [3,3] [===========================================================>---------------------------] 69% est: 0s
plot: [3,4] [================================================================>----------------------] 75% est: 0s
plot: [4,1] [======================================================================>----------------] 81% est: 0s `geom_smooth()` using formula 'y ~ x'
plot: [4,2] [===========================================================================>-----------] 88% est: 0s `geom_smooth()` using formula 'y ~ x'
plot: [4,3] [=================================================================================>-----] 94% est: 0s `geom_smooth()` using formula 'y ~ x'
plot: [4,4] [=======================================================================================]100% est: 0s
$`yeGFP-CLN2`
plot: [1,1] [====>----------------------------------------------------------------------------------] 6% est: 0s
plot: [1,2] [==========>----------------------------------------------------------------------------] 12% est: 0s
plot: [1,3] [===============>-----------------------------------------------------------------------] 19% est: 1s
plot: [1,4] [=====================>-----------------------------------------------------------------] 25% est: 1s
plot: [2,1] [==========================>------------------------------------------------------------] 31% est: 1s `geom_smooth()` using formula 'y ~ x'
plot: [2,2] [================================>------------------------------------------------------] 38% est: 1s
plot: [2,3] [=====================================>-------------------------------------------------] 44% est: 1s
plot: [2,4] [===========================================>-------------------------------------------] 50% est: 1s
plot: [3,1] [================================================>--------------------------------------] 56% est: 1s `geom_smooth()` using formula 'y ~ x'
plot: [3,2] [=====================================================>---------------------------------] 62% est: 1s `geom_smooth()` using formula 'y ~ x'
plot: [3,3] [===========================================================>---------------------------] 69% est: 0s
plot: [3,4] [================================================================>----------------------] 75% est: 0s
plot: [4,1] [======================================================================>----------------] 81% est: 0s `geom_smooth()` using formula 'y ~ x'
plot: [4,2] [===========================================================================>-----------] 88% est: 0s `geom_smooth()` using formula 'y ~ x'
plot: [4,3] [=================================================================================>-----] 94% est: 0s `geom_smooth()` using formula 'y ~ x'
plot: [4,4] [=======================================================================================]100% est: 0s
$yeGFP
plot: [1,1] [====>----------------------------------------------------------------------------------] 6% est: 0s
plot: [1,2] [==========>----------------------------------------------------------------------------] 12% est: 0s
plot: [1,3] [===============>-----------------------------------------------------------------------] 19% est: 1s
plot: [1,4] [=====================>-----------------------------------------------------------------] 25% est: 1s
plot: [2,1] [==========================>------------------------------------------------------------] 31% est: 1s `geom_smooth()` using formula 'y ~ x'
plot: [2,2] [================================>------------------------------------------------------] 38% est: 1s
plot: [2,3] [=====================================>-------------------------------------------------] 44% est: 0s
plot: [2,4] [===========================================>-------------------------------------------] 50% est: 0s
plot: [3,1] [================================================>--------------------------------------] 56% est: 0s `geom_smooth()` using formula 'y ~ x'
plot: [3,2] [=====================================================>---------------------------------] 62% est: 0s `geom_smooth()` using formula 'y ~ x'
plot: [3,3] [===========================================================>---------------------------] 69% est: 0s
plot: [3,4] [================================================================>----------------------] 75% est: 0s
plot: [4,1] [======================================================================>----------------] 81% est: 0s `geom_smooth()` using formula 'y ~ x'
plot: [4,2] [===========================================================================>-----------] 88% est: 0s `geom_smooth()` using formula 'y ~ x'
plot: [4,3] [=================================================================================>-----] 94% est: 0s `geom_smooth()` using formula 'y ~ x'
plot: [4,4] [=======================================================================================]100% est: 0s



ggsave(plot = gfp.area.cor.ggPair$`yeGFP-mODC`, filename = "gfp_mODC_4Cor_ggP.png", path = "~/plots/paper1/figures/fig_2/", width = 4, height = 3.5)
ggsave(plot = gfp.area.cor.ggPair$`yeGFP-mODC`, filename = "gfp_mODC_4Cor_ggP.pdf", path = "~/plots/paper1/figures/fig_2/", width = 4, height = 3.5)
ggsave(plot = gfp.area.cor.ggPair$`yeGFP-CLN2`, filename = "gfp_cln2_4Cor_ggP.png", path = "~/plots/paper1/figures/fig_2/", width = 4, height = 3.5)
ggsave(plot = gfp.area.cor.ggPair$`yeGFP-CLN2`, filename = "gfp_cln2_4Cor_ggP.pdf", path = "~/plots/paper1/figures/fig_2/", width = 4, height = 3.5)
ggsave(plot = gfp.area.cor.ggPair$`yeGFP`, filename = "gfp_4Cor_ggP.png", path = "~/plots/paper1/figures/fig_2/", width = 4, height = 3.5)
ggsave(plot = gfp.area.cor.ggPair$`yeGFP`, filename = "gfp_4Cor_ggP.pdf", path = "~/plots/paper1/figures/fig_2/", width = 4, height = 3.5)
test.ggP <- (wrap_elements(ggmatrix_gtable(gfp.area.cor.ggPair$`yeGFP-mODC`))|wrap_elements(ggmatrix_gtable(gfp.area.cor.ggPair$`yeGFP-CLN2`))| wrap_elements(ggmatrix_gtable(gfp.area.cor.ggPair$yeGFP)))+
plot_annotation(tag_levels = "A")
ggsave(plot = test.ggP, filename = "test_ggP.pdf", path = "~/plots/paper1/figures/fig_2/", width = 6.5, height = 3)
#area vs pup1 vs dna vs gfp
df_corSup_fig <- mlr.df %>%
filter(degron %in% c("mODC.2", "cln2.3", "cln2.4", "stable.2", "stable.3")) %>%
dplyr::select(t.half,
regressors,
degron) %>%
rename(
"GFP" = "gfp.mean.bg.af.sub.new",
"Pup1-tDimer" = "rfp.mean.bg.sub.puncta",
"DNA" = "dapi.sum.bg.sub.puncta",
"Area" = "area"
) %>%
dplyr::select(Area, DNA, `Pup1-tDimer`,GFP,degron ) %>%
mutate(
degron = case_when(
degron %in% c("cln2.3", "cln2.4") ~ "yeGFP-CLN2",
degron %in% c("mODC.2") ~ "yeGFP-mODC",
degron %in% c("stable.2", "stable.3") ~ "yeGFP"
),
degron = factor(degron , levels = c("yeGFP-mODC", "yeGFP-CLN2", "yeGFP"))
)
area.pup1 <- df_corSup_fig %>%
filter(degron == "yeGFP-mODC") %>%
ggplot(.,aes(x = Area, y = `Pup1-tDimer`))+
ggpointdensity::geom_pointdensity(size = 0.2, guides = F) +
stat_cor(size = 3) +
geom_smooth(method = "lm", se = FALSE, color = "red4", size = 0.5)+
theme_bw() +
labs(x = NULL) +
theme(text = element_text(size = 8),
axis.text.x = element_text(angle = 30),
legend.key.size = unit(2,"mm"),
legend.position = "none",
axis.line = element_line(size = 0.1))+
ylab("Pup1-tDimer [A.U]")+
xlab("Area [A.U]")
Warning: Ignoring unknown parameters: guides
dna.pup1 <- df_corSup_fig %>%
filter(degron == "yeGFP-mODC") %>%
ggplot(.,aes(x = DNA, y = `Pup1-tDimer`))+
ggpointdensity::geom_pointdensity(size = 0.2, guides = F) +
stat_cor(size = 3) +
geom_smooth(method = "lm", se = FALSE, color = "red4", size = 0.5)+
theme_bw() +
labs(x = NULL) +
theme(text = element_text(size = 8),
axis.text.x = element_text(angle = 30),
legend.key.size = unit(2,"mm"),
legend.position = "none",
axis.line = element_line(size = 0.1))+
ylab("Pup1-tDimer [A.U]")+
xlab("DNA [A.U]")
Warning: Ignoring unknown parameters: guides
dna.area <- df_corSup_fig %>%
filter(degron == "yeGFP-mODC") %>%
ggplot(.,aes(x = DNA, y = Area))+
ggpointdensity::geom_pointdensity(size = 0.2, guides = F) +
stat_cor(size = 3) +
geom_smooth(method = "lm", se = FALSE, color = "red4", size = 0.5)+
theme_bw() +
labs(x = NULL) +
theme(text = element_text(size = 8),
axis.text.x = element_text(angle = 30),
legend.key.size = unit(2,"mm"),
legend.position = "none",
axis.line = element_line(size = 0.1))+
ylab("Area [A.U]")+
xlab("DNA [A.U]")
Warning: Ignoring unknown parameters: guides
gfpInt.cor.plt <- df_corSup_fig %>%
pivot_longer(cols = 1:3) %>%
# filter(degron == "yeGFP-mODC") %>%
ggplot(.,aes(x = value, y = GFP))+
ggpointdensity::geom_pointdensity(size = 0.2, guides = F) +
stat_cor(size = 3) +
geom_smooth(method = "lm", se = FALSE, color = "red4", size = 0.5)+
facet_wrap(degron~name, scales = "free")+
theme_bw() +
labs(x = NULL) +
theme(text = element_text(size = 8),
axis.text.x = element_text(angle = 30),
legend.key.size = unit(2,"mm"),
legend.position = "none",
axis.line = element_line(size = 0.1))+
xlab("A.U")
Warning: Ignoring unknown parameters: guides
test_cor
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

#Partial correlations ppcor
#partial correlations
ppCor.list <- mlr.df2 %>%
filter(treatment == "none") %>% split(.$degron) %>%
map(.,function(a){
a <- a %>%
dplyr::select(t.half, area, dapi.sum.bg.sub.puncta, rfp.mean.bg.sub.puncta, gfp.mean.bg.af.sub.new) %>%
na.omit()
ppcor::pcor(a)
})
#correlations
cor.all <- mlr.df2 %>%
filter(treatment == "none") %>%
split(.$degron) %>%
map(.,function(a){
a <- a %>%
dplyr::select(t.half, area, dapi.sum.bg.sub.puncta,rfp.mean.bg.sub.puncta, gfp.mean.bg.af.sub.new) %>%
na.omit()
cor(a)
})
ppCor.df <- ppCor.list %>%
map(.,function(a){
a$estimate %>%
as.data.frame() %>%
rownames_to_column(var = "regressor") %>%
dplyr::select(t.half, regressor) %>%
left_join(.,a$p.value%>%
as.data.frame() %>%
rownames_to_column(var = "regressor") %>%
rename("p.value" = "t.half") %>%
dplyr::select(p.value, regressor), by = "regressor")
}) %>%
bind_rows(.id = "degron")
single.core.df <- cor.all %>%
map(.,function(a){
a%>%
as.data.frame() %>%
rownames_to_column(var = "regressor") %>%
dplyr::select(t.half, regressor)
}) %>%
bind_rows(.id = "degron") %>%
rename("single.core"="t.half")
ppCoor.fig <- ppCor.df %>%
left_join(.,single.core.df, by = c("degron","regressor")) %>%
rename("Partial-Pariwise"="t.half",
"Standard-Pariwise" = "single.core") %>%
pivot_longer(cols = c("Partial-Pariwise","Standard-Pariwise")) %>%
mutate(regressor = case_when(regressor == "gfp.mean.bg.af.sub.new" ~ "GFP" ,
regressor == "area" ~ "Area",
regressor == "rfp.mean.bg.sub.puncta" ~ "Pup1",
regressor == "dapi.sum.bg.sub.puncta" ~ "DNA" ,
regressor == "t.half" ~ "half-life"),
degron = factor(degron, levels = c("yeGFP-mODC","yeGFP-CLN2","yeGFP")),
name = factor(name , levels = c("Standard-Pariwise", "Partial-Pariwise")),
sig = stars.pval(p.value),
sig = ifelse(sig == " ", "NS", sig)) %>%
# filter(p.value < 0.05) %>%
filter(regressor != "half-life") %>%
ggplot(.,aes(x = regressor , y = value, fill = name))+
geom_col(width = 0.5, position = "dodge")+
facet_wrap(~degron)+
geom_hline(yintercept = 0, size = 0.1)+
scale_y_continuous(breaks = seq(-0.5,0.5, by = 0.1))+
theme_pubr()+
theme(text = element_text(size = 8),
legend.key.size = unit(4,"mm"),
legend.position = "top",
axis.line = element_line(size = 0.1),
strip.background = element_blank())+
scale_fill_manual(values = c("lightsteelblue4" ," steelblue4"), name = NULL)+
ylab("Correlation with Half-life ")+
xlab("Cellular features")+
labs(title = "C")
ppCoor.fig

#patchwork for 4 cellular features (area, gfp, dapi, pup1) and
t-half
gfp.4cor.hf.ppCor.plt
`geom_smooth()` using formula 'y ~ x'

ggsave(plot = gfp.4cor.hf.ppCor.plt, filename = "gfp_4cor_halfLife_ppcor.png", path = "~/plots/paper1/figures/fig_2/", width = 7, height = 9)
`geom_smooth()` using formula 'y ~ x'
#with protein inhibion
ppCor.PrtIn.list <- mlr.df %>%
filter(treatment != "none") %>%
filter(ifelse(treatment == "5uM", t.half < 138.6294, t.half == t.half)) %>%
split(.$treatment) %>%
map(.,function(a){
a <- a %>%
dplyr::select(t.half, area, dapi.sum.bg.sub.puncta, rfp.mean.bg.sub.puncta, gfp.mean.bg.af.sub.new) %>%
na.omit()
ppcor::pcor(a)
})
cor.PrtIn.all <- mlr.df %>%
filter(treatment != "none") %>%
filter(ifelse(treatment == "5uM", t.half < 138.6294, t.half == t.half)) %>%
split(.$treatment) %>%
map(.,function(a){
a <- a %>%
dplyr::select(t.half, area, dapi.sum.bg.sub.puncta, rfp.mean.bg.sub.puncta, gfp.mean.bg.af.sub.new) %>%
na.omit()
cor(a)
})
ppCor.PrtIn.df <- ppCor.PrtIn.list %>%
map(.,function(a){
a$estimate %>%
as.data.frame() %>%
rownames_to_column(var = "regressor") %>%
dplyr::select(t.half, regressor) %>%
left_join(.,a$p.value%>%
as.data.frame() %>%
rownames_to_column(var = "regressor") %>%
rename("p.value" = "t.half") %>%
dplyr::select(p.value, regressor), by = "regressor")
}) %>%
bind_rows(.id = "treatment")
single.core.Prtn.df <- cor.PrtIn.all %>%
map(.,function(a){
a%>%
as.data.frame() %>%
rownames_to_column(var = "regressor") %>%
dplyr::select(t.half, regressor)
}) %>%
bind_rows(.id = "treatment") %>%
rename("single.core"="t.half")
correlation of pup1 with mODC with treated with proteasome
inhibitors

#when you normalize pup1 with the area
mlr.df %>%
mutate(dm = log10(dm)) %>%
filter(degron %in% c("mODC.2", "cln2.3", "cln2.4", "stable.2", "stable.3")) %>%
dplyr::select(t.half,
regressors,
degron) %>%
rename(
"GFP" = "gfp.mean.bg.af.sub.new",
"Pup1-tDimer" = "rfp.mean.bg.sub.puncta",
"DNA" = "dapi.sum.bg.sub.puncta",
"Area" = "area"
) %>%
mutate(norm.pup1 = `Pup1-tDimer`/Area) %>%
pivot_longer(cols = c(2:7,9)) %>%
mutate(degron = case_when(
degron %in% c("cln2.3", "cln2.2", "cln2", "cln2.4") ~ "yeGFP-CLN2",
degron %in% c("mODC.2", "mODC") ~ "yeGFP-mODC",
degron %in% c("stable", "stable.2", "stable.3") ~ "yeGFP"
),
degron = factor(degron , levels = c("yeGFP-mODC", "yeGFP-CLN2", "yeGFP"))
) %>%
filter(!(name %in% c("shape","dm"))) %>%
ggplot(., aes(x = value, y = t.half)) +
ggpointdensity::geom_pointdensity(size = 0.2, guides = F) +
stat_cor(size = 2) +
geom_smooth(method = "lm", se = FALSE, color = "red4", size = 0.5)+
facet_wrap(degron ~ name, scales = "free", ncol = 4) +
theme_bw() +
labs(x = NULL) +
theme(text = element_text(size = 8),
axis.text.x = element_text(angle = 30),
legend.key.size = unit(2,"mm"),
legend.position = "none")+
ylab("Half-Life [min.]")+
xlab("A.U")
---
title: "Correlations"
output: html_notebook
---
```{r}
library(ggplot2)
library(tidyverse)
library(GGally)
library(ggpubr)
library(latex2exp)
library(patchwork)
library(gtools)
```

#load the dataframes for the figure
```{r}
aic.df <- read_csv("~/plots/all_data/aic.csv")
```


remove cells in the edges :
pos.x > 45 & < 1150
pos.y > 45 & < 1000
##add the replicate info 
```{r}
aic.df <- aic.df %>% 
  mutate(exp.field = paste0(str_split(cell.id, "_", simplify = T)[,2],"_",
                            str_split(cell.id, "_", simplify = T)[,3]),
    colony = case_when(exp.field %in% c("20min_s3", "20min_s4") ~ "Replicate 1", 
                            exp.field %in% c("20min_s5", "20min_s6") ~ "Replicate 2",
                            exp.field %in% c("20min_s7", "20min_s8" , "20min_s9") ~ "Replicate 3"))

aic.df <- aic.df %>% 
  mutate(colony = ifelse(degron == "stable" & red == "pup1-rfp", 
                         case_when(exp.field %in% c("20min_s4", "20min_s5") ~ "Replicate 1",
                                   exp.field %in% c("20min_s6", "20min_s7") ~ "Replicate 2",
                                   exp.field %in% c("20min_s8", "20min_s9") ~ "Replicate 3"), colony)) 
  
```

##Cellular attributes
```{r}
pup1.cell.attr <- read_csv("~/plots/all_data/all_pup1_cell_attr.csv")
```

###pup1-RFP background
```{r}
pup1.cell.attr <- pup1.cell.attr %>% 
  mutate(exp.field = paste0(str_split(cell.id, "_", simplify = T)[,2],"_",
                            str_split(cell.id, "_", simplify = T)[,3]),
    colony = case_when(exp.field %in% c("20min_s3", "20min_s4") ~ "Replicate 1", 
                            exp.field %in% c("20min_s5", "20min_s6") ~ "Replicate 2",
                            exp.field %in% c("20min_s7", "20min_s8" , "20min_s9") ~ "Replicate 3"))

pup1.cell.attr <- pup1.cell.attr %>% 
  mutate(colony = ifelse(degron == "stable" & red == "pup1-rfp", 
                         case_when(exp.field %in% c("20min_s4", "20min_s5") ~ "Replicate 1",
                                   exp.field %in% c("20min_s6", "20min_s7") ~ "Replicate 2",
                                   exp.field %in% c("20min_s8", "20min_s9") ~ "Replicate 3"), colony)) 
  
```

```{r}
#removing the 40min and 60min experiments from the stable gfp experiments
pup1.cell.attr <- pup1.cell.attr %>%
  mutate(exp = str_split(exp.field, "_", simplify = T)[,1]) %>% split(.$exp)
  filter(exp == "20min")
```

Adding the gfp.sum column for the stable GFP experiment
```{r}
pup1.cell.attr <- read_csv("~/plots/pup1-rfp-gfp-decay/4-28-21-8hrGFP/data/gfp_stable_filtered.csv") %>% 
  filter(image.no == 1) %>% 
  dplyr::select(cell.id, gfpSumBgAFsub) %>% 
  mutate(cell.id = paste0(cell.id,"_","stable_pup1-rfp_none")) %>% 
  dplyr::rename("gfp.sum.bg.af.sub" = "gfpSumBgAFsub") %>% 
  left_join(pup1.cell.attr %>% 
              filter(degron == "stable") %>% 
              dplyr::select(-gfp.sum.bg.af.sub),., by = "cell.id") %>% 
  bind_rows(pup1.cell.attr %>% 
              filter(degron != "stable"),.)
```

removing multiple entries of the cell because some cell have more than one, 
```{r}
multiple.pup1 <- pup1.cell.attr %>% 
  mutate(dapi.mean.bg.sub.puncta = ifelse(is.na(dapi.mean.bg.sub.puncta), 0, dapi.mean.bg.sub.puncta) , 
         rfp.mean.bg.sub.puncta = ifelse(is.na(rfp.mean.bg.sub.puncta), 0 , rfp.mean.bg.sub.puncta)) %>% 
  filter(rfp.mean.bg.sub.puncta > 0) %>% 
  group_by(cell.id) %>% 
  tally() %>% 
  filter(n>1)

pup1.cell.attr<- pup1.cell.attr %>% 
  group_by(cell.id) %>% 
  mutate(rfp.mean.bg.sub.puncta = ifelse(cell.id %in% multiple.pup1$cell.id , mean(rfp.mean.bg.sub.puncta), rfp.mean.bg.sub.puncta),
         rfp.sum.bg.sub.puncta = ifelse(cell.id %in% multiple.pup1$cell.id, sum(rfp.mean.bg.sub.puncta), rfp.sum.bg.sub.puncta)) %>% 
    distinct(cell.id, .keep_all = TRUE) 
  
```

#proteasome inhibition experiments
```{r}
protInhi.attr <- read_csv("~/plots/all_data/all_mg135_attr.csv") %>% 
  rename("cell.id" = "unique.trackID") %>% 
  filter(timepoint == 1) %>% 
  mutate(cell.id = paste0(cell.id ,"_",degron,"_",red,"_",treatment)) %>% 
  mutate(exp.field = paste0(str_split(cell.id, "_", simplify = T)[,2],"_",
                            str_split(cell.id, "_", simplify = T)[,3]),
    colony = case_when(exp.field %in% c("20min_s3", "20min_s4") ~ "Replicate 1", 
                            exp.field %in% c("20min_s5", "20min_s6") ~ "Replicate 2",
                            exp.field %in% c("20min_s7", "20min_s8" , "20min_s9") ~ "Replicate 3"))

multiple.proInh <- protInhi.attr %>% 
  mutate(dapi.mean.bg.sub.puncta = ifelse(is.na(dapi.mean.bg.sub.puncta), 0, dapi.mean.bg.sub.puncta) , 
         rfp.mean.bg.sub.puncta = ifelse(is.na(rfp.mean.bg.sub.puncta), 0 , rfp.mean.bg.sub.puncta)) %>% 
  filter(rfp.mean.bg.sub.puncta > 0) %>% 
  group_by(cell.id) %>% 
  tally() %>% 
  filter(n>1)

protInhi.attr <- protInhi.attr %>% 
  group_by(cell.id) %>% 
  mutate(rfp.mean.bg.sub.puncta = ifelse(cell.id %in% multiple.proInh$cell.id , mean(rfp.mean.bg.sub.puncta), rfp.mean.bg.sub.puncta),
         rfp.sum.bg.sub.puncta = ifelse(cell.id %in% multiple.proInh$cell.id, sum(rfp.mean.bg.sub.puncta), rfp.sum.bg.sub.puncta)) %>% 
    distinct(cell.id, .keep_all = TRUE)  
  
```

Adding the protein inhibition experiment data
```{r}
pup1.proInhi.attr <- pup1.cell.attr %>% 
  bind_rows(.,protInhi.attr %>% 
              dplyr::select(-time, 
                            -timepoint,
                            -image.no, 
                            -ln.gfp, 
                            -ln.gfp.dif,
                            -trackID,
                            -gfp.intensity.center,
                            -gfp.int.mean,
                            -gfp.int.median,
                           -gfp.int.sum,
                           -no.of.triangles,
                           -field,
                           -time.dif.gfp,
                           -real.time.gfp,
                           -sample,
                           -avg.gfp.bg,
                           -Min_gfp,
                           -gfp.mean.bg.sub,
                           -value,
                           -t80,
                           -t95,
                           -threshold,
                           -threshold_95,
                           -gfp.mean.bg.af.sub,
                           -threshold_80,
                           -Mean_gfp,
                           -image,
                           -experiment,
                           -gfp.sum.bg.sub))
```


#keeping the parameters estimated from the 2-parameter with maturation model
```{r}
#only dy.dm
dy.pup1.rep1.mat <- aic.df %>% 
  filter(red == "pup1-rfp") %>% 
  filter(ifelse(degron %in% c("stable","stable.2","stable.3"), dy < 0.1, dy < 0.5)) %>% 
  group_by(cell.id) %>% 
  filter(model == "dy.dm") %>% 
  mutate(dm = ifelse(is.na(dm), Inf, dm)) %>%
  filter( dm > 0.000015)
```

#count the number of cells in each experiment 
```{r}
dy.pup1.rep1.mat %>% group_by(degron, treatment) %>% tally()
```
#creating the df for the MLR
```{r}
mlr.df <- dy.pup1.rep1.mat %>% 
  #left join with the df with cellular attributes
  left_join(.,pup1.proInhi.attr, by = c("cell.id","red","treatment","degron", "exp.field","colony")) %>% 
  #remove cells with no rfp puncta info 
  mutate(rfp.mean.bg.sub.puncta = ifelse(is.na(rfp.mean.bg.sub.puncta), 0, rfp.mean.bg.sub.puncta)) %>% 
  filter(rfp.mean.bg.sub.puncta > 0)
```

#checking how many cells there are in each experiment 
```{r}
mlr.df %>% group_by(degron, treatment) %>% tally()
```

##Removing the columns that I wont regress upon
```{r}
mlr.df <- mlr.df %>% 
  filter(pos.x > 45 & pos.x < 1150,
  pos.y > 45 & pos.y < 1000) %>%
  dplyr::select(-f, 
         -rfp.mean.af.sub.puncta,
         -rfp.sum.af.sub.puncta, 
         -dapi.sum.af.sub.puncta,
         -dapi.mean.af.sub.puncta,
         -model, 
         -k,
         -fevals,
         -gevals, 
         -niter, 
         -convcode, 
         -kkt1,
         -kkt2,
         -value,
         -xtime, 
         # -treatment,
         -rfp.mean.bg.sub,
         -rfp.sum.bg.sub,
         -dapi.mean.bg.sub,
         -dapi.sum.bg.sub,
         -dapi.mean.bg.sub.puncta,
         -area.puncta,
         -BB_B,
         -BB_C,
         -pos.x,
         -pos.y,
         -gfp.sum.bg.af.sub,
         -rfp.sum.bg.sub.puncta,
         -no.of.voxels,
         -volume,
         -spheracity) %>% 
  mutate(shape = Elip_B/Elip_C, 
         t.half = log(2)/dy) %>%
  ungroup() %>%
  dplyr::select(-cell.id, 
                -red,
                -exp.field, 
                -aic,
                -Elip_B,
                -Elip_C, 
                -exp) 
```

#Regressors to use for MLR
```{r}
regressors <- c("dm",
                "gfp.mean.bg.af.sub.new",
                "area",
                "rfp.mean.bg.sub.puncta",
                "dapi.sum.bg.sub.puncta",
                "shape")
```


#df where the two experiments of cln2 and stable-GFP are clubbed 
```{r}
mlr.df2 <- mlr.df %>% 
  filter(degron %in% c("cln2.3","mODC.2","stable.3","stable.2","cln2.4")) %>% 
  mutate(degron = case_when(degron %in% c("cln2.3","cln2.2","cln2","cln2.4") ~ "yeGFP-CLN2",
                            degron %in% c("mODC.2", "mODC")  ~ "yeGFP-mODC",
                            degron %in% c("stable","stable.2","stable.3") ~ "yeGFP")) 
  
```


#df to be used with step function
```{r}
temp.df.list <- mlr.df2 %>% 
    filter(treatment == "none") %>%  
  # mutate( dm = log10(dm)) %>% 
  split(.$degron)
```


#using stepAIC from MASS
```{r}
library(MASS)

full.stepAIC.mass <- lapply(temp.df.list, function(a){
  n <- nrow(a)
  lm.obj <- lm(t.half ~ ., data = a %>% 
     dplyr::select(regressors,
                   t.half,
                   -dy, 
                   -treatment))
     
 stepAIC(object = lm.obj, 
     direction = "backward", 
     trace = T)
})
```

#step AIC with all the fp markers + cell shape 
```{r}
full.stepAIC <- lapply(temp.df.list, function(a){
  n <- nrow(a)
 step(lm(t.half ~ ., data = a %>% 
     dplyr::select(regressors,
                   t.half,
                   -dy, 
                   -treatment)), 
     direction = "backward", 
     trace = T,
     # k = log(n),
     k =2 ) #k = 2 when small dataset and k = log(n) where n = no. of observations for when the dataset is large
})


```


#step AIC with only the fluorescent markers 
```{r}
full.stepAIC.fp <- lapply(temp.df.list, function(a){
  n <- nrow(a)
 step(lm(t.half ~ ., data = a %>% 
     dplyr::select(regressors.fp,
                   t.half,
                   -dy, 
                   -treatment)), 
     direction = "backward", 
     trace = T,
     k = 2)
})

```

Variance in t-half explained by each regressor
using anova

```{r}
var.explained <- full.stepAIC %>% 
  map(., anova %>% as.data.frame) %>% 
  bind_rows(.id = "sample") %>% 
  rownames_to_column(var = "predictor") %>% 
  # filter(sample %in% c("mODC.2","cln2.3","cln2.4","stable.2","stable.3")) %>% 
  group_by(sample) %>% 
  mutate(var.exp = (value.Sum.Sq/sum(value.Sum.Sq))*100, 
         predictor = str_split(predictor, pattern = "\\.\\.\\.", simplify = T)[,1], 
         predictor = case_when(predictor == "gfp.mean.bg.af.sub.new" ~ "GFP" ,
                               predictor == "area" ~ "Area",
                               predictor == "rfp.mean.bg.sub.puncta" ~ "Pup1",
                               predictor == "dapi.sum.bg.sub.puncta" ~ "DNA" ,
                               predictor == "shape" ~ "Shape", 
                               predictor == "dm" ~ "Rate of Maturation"
                               , 
                               TRUE~predictor)) %>% 
  split(.$sample)

var.explained
```

#plotting the variance explained 
```{r}
remain.reg <- data.frame(sample = c("yeGFP-CLN2","yeGFP-CLN2","yeGFP","yeGFP"), 
                         predictor = c("Rate of Maturation", "Shape","Shape","Pup1"), 
                         var.exp = rep(0,4),
                         value.Pr..F. = rep(0,4))
var.exp.plt <- var.explained %>% 
  bind_rows() %>% 
  bind_rows(.,remain.reg) %>% 
  filter(predictor != "Residuals") %>% 
  dplyr::select(sample, predictor, var.exp,value.Pr..F.) %>% 
  mutate(sample = factor(sample , levels = c("yeGFP-mODC","yeGFP-CLN2","yeGFP"))) %>% 
  # mutate(var.pal = ifelse(value.Pr..F. < 0.05, "<0.05",">0.05")) %>% 
  arrange(sample) %>% 
  ggplot(.,aes(x = predictor, y = var.exp ))+
  geom_col(width = 0.5)+
  facet_wrap(~sample, scales = "free")+
  scale_x_discrete(labels = function(x) str_wrap(x, width = 5))+
  theme_pubr()+
  theme(text = element_text(size = 8),
        axis.line = element_line(size = 0.1),
        strip.background = element_blank())+
  ylim(0,20)+
  ylab("Variance explained (%)")+
  xlab("Cellular features")+
  labs(title = "B")
var.exp.plt
```


#plots: 
```{r}
gfp.6.cor.plt <- mlr.df %>%
  mutate(dm = log10(dm)) %>% 
  filter(degron %in% c("mODC.2", "cln2.3", "cln2.4", "stable.2", "stable.3")) %>%
  dplyr::select(t.half,
         regressors,
         degron) %>%
  rename(
    "GFP" = "gfp.mean.bg.af.sub.new",
    "Pup1-tDimer" = "rfp.mean.bg.sub.puncta",
    "DNA" = "dapi.sum.bg.sub.puncta",
    "Area" = "area"
  ) %>%
  pivot_longer(cols = 2:7) %>%
  mutate(degron = case_when(
      degron %in% c("cln2.3", "cln2.2", "cln2", "cln2.4") ~ "yeGFP-CLN2",
      degron %in% c("mODC.2", "mODC")  ~ "yeGFP-mODC",
      degron %in% c("stable", "stable.2", "stable.3") ~ "yeGFP"
    ),
    degron = factor(degron , levels = c("yeGFP-mODC", "yeGFP-CLN2", "yeGFP"))
  ) %>%
  filter(!(name %in% c("shape","dm"))) %>% 
  ggplot(., aes(x = value, y = t.half)) +
      ggpointdensity::geom_pointdensity(size = 0.2, guides = F) +
      stat_cor(size = 2) +
      geom_smooth(method = "lm", se = FALSE, color = "red4", size = 0.5)+
      facet_wrap(degron ~ name, scales = "free", ncol = 4) +
      theme_bw() +
      labs(x = NULL) +
      theme(text = element_text(size = 8),
            axis.text.x = element_text(angle = 30),
            legend.key.size = unit(2,"mm"), 
            legend.position = "none", 
            axis.line = element_line(size = 0.1))+
  ylab("Half-life [min.]")+
  xlab("A.U")+
  labs(title = "A")
  
gfp.6.cor.plt
```
with protein inhibition
```{r}
 mlr.df %>%
  mutate(dm = log10(dm)) %>% 
  filter(degron %in% c("mODC")) %>%
  # filter(!(treatment %in% c("none", "dmso1","50uM")), 
         # ifelse(treatment == "5uM", t.half < 138.6294, t.half == t.half)) %>%  
  dplyr::select(t.half,
         regressors,
         degron,
         treatment) %>%
  rename(
    "GFP" = "gfp.mean.bg.af.sub.new",
    "Pup1-tDimer" = "rfp.mean.bg.sub.puncta",
    "DNA" = "dapi.sum.bg.sub.puncta",
    "Area" = "area"
  ) %>%
  pivot_longer(cols = 2:7) %>%
  mutate(degron = case_when(
    degron %in% c("mODC")  ~ "yeGFP-mODC"),
    treatment = factor(treatment, levels = c("none","dmso1","dmso2","1uM","2.5uM","5uM","50uM"))) %>%
  filter(!(name %in% c("shape","dm")),
         !(treatment %in% c("none","dmso1","50uM")), 
         t.half < 130) %>% 
  
  ggplot(., aes(x = value, y = t.half)) +
      ggpointdensity::geom_pointdensity(size = 0.2, guides = F) +
      stat_cor(size = 2) +
      geom_smooth(method = "lm", se = FALSE, color = "red4", size = 0.5)+
      facet_grid(treatment ~ name, scales = "free") +
      theme_bw() +
      labs(x = NULL) +
      theme(text = element_text(size = 8),
            axis.text.x = element_text(angle = 30),
            legend.key.size = unit(2,"mm"), 
            legend.position = "none")+
  ylab("Half-Life [min.]")+
  xlab("A.U")

```

#stepAIC with proteasome inhibition experiments
With all fp regressors
```{r}
temp.df.list.dmso <- mlr.df %>% 
    filter(treatment != "none", t.half < 130) %>%  
  split(.$treatment)

full.stepAIC.fp.dmso <- lapply(temp.df.list.dmso, function(a){
 step(lm(t.half ~ ., data = a %>% 
     dplyr::select(regressors.fp,
                   t.half,
                   -dy, 
                   -treatment)), 
     direction = "backward", trace = T)
})

```

with fp regressors + celluar shape 
```{r}
temp.df.list.dmso <- mlr.df %>% 
    filter(treatment != "none", t.half < 130) %>%  
  split(.$treatment)

full.stepAIC.dmso <- lapply(temp.df.list.dmso, function(a){
 step(lm(t.half ~ ., data = a %>% 
     dplyr::select(regressors.fp,
                   t.half,
                   -dy, 
                   -treatment)), 
     direction = "backward", trace = T)
})


```

Variance in t-half explained by each regressor
```{r}
full.stepAIC.dmso %>% 
  map(., anova %>% as.data.frame) %>% 
  bind_rows(.id = "sample") %>% 
  rownames_to_column(var = "predictor") %>% 
  # filter(sample %in% c("mODC.2","cln2.3","cln2.4","stable.2","stable.3")) %>% 
  group_by(sample) %>% 
  mutate(var.exp = (value.Sum.Sq/sum(value.Sum.Sq))*100) %>% 
  split(.$sample)

full.stepAIC.fp.dmso %>% 
  map(., anova %>% as.data.frame) %>% 
  bind_rows(.id = "sample") %>% 
  rownames_to_column(var = "predictor") %>% 
  # filter(sample %in% c("mODC.2","cln2.3","cln2.4","stable.2","stable.3")) %>% 
  group_by(sample) %>% 
  mutate(var.exp = (value.Sum.Sq/sum(value.Sum.Sq))*100) %>% 
  split(.$sample)

```

```{r}
full.stepAIC.dmso %>% 
  map(., anova %>% as.data.frame) %>% 
  bind_rows(.id = "sample") %>% 
  rownames_to_column(var = "predictor") %>% 
  # filter(sample %in% c("mODC.2","cln2.3","cln2.4","stable.2","stable.3")) %>% 
  group_by(sample) %>% 
  mutate(var.exp = (value.Sum.Sq/sum(value.Sum.Sq))*100, 
         predictor = str_split(predictor, pattern = "\\.\\.\\.", simplify = T)[,1], 
         predictor = case_when(predictor == "gfp.mean.bg.af.sub.new" ~ "GFP" ,
                               predictor == "area" ~ "Area",
                               predictor == "rfp.mean.bg.sub.puncta" ~ "Pup1",
                               predictor == "dapi.sum.bg.sub.puncta" ~ "DNA" ,
                               predictor == "shape" ~ "Shape", 
                               predictor == "dm" ~ "Rate of Maturation"
                               , 
                               TRUE~predictor)) %>% 
  split(.$sample)
```

```{r}
mlr.df %>%
  mutate(dm = log10(dm)) %>% 
  filter(degron %in% c ("mODC") ) %>%
  filter(!(treatment %in% c("none", "dmso1","50uM")), 
         ifelse(treatment == "5uM", t.half < 138.6294, t.half == t.half)) %>% 
  dplyr::select(t.half,
         regressors,
         degron,
         treatment) %>% 
  ggplot(.,aes(x = area, y = rfp.mean.bg.sub.puncta))+
  geom_point()+
  geom_smooth(method = "lm", se = F)+
  facet_wrap(~treatment)+
  stat_cor()
```


#ggpairs 
```{r}
#to be used with ggpairs
my_fn <- function(data, mapping, ...){
  p <- ggplot(data = data, mapping = mapping) + 
    ggpointdensity::geom_pointdensity(size = 0.1)+
    # geom_point(size = 0.1, alpha = 0.1) + 
    geom_smooth(method="lm",  color="red4", se = FALSE, lwd = 0.5)+
    theme(legend.position = "none")
  p
}

gfp.area.cor.ggPair <- mlr.df %>%
  filter(degron %in% c("mODC.2", "cln2.3", "cln2.4", "stable.2", "stable.3")) %>%
  dplyr::select(t.half,
         regressors,
         degron) %>%
  rename(
    "GFP" = "gfp.mean.bg.af.sub.new",
    "Pup1-tDimer" = "rfp.mean.bg.sub.puncta",
    "DNA" = "dapi.sum.bg.sub.puncta",
    "Area" = "area"
  ) %>%
  dplyr::select(Area, DNA, `Pup1-tDimer`,GFP,degron ) %>% 
  mutate(
    degron = case_when(
      degron %in% c("cln2.3", "cln2.4") ~ "yeGFP-CLN2",
      degron %in% c("mODC.2")  ~ "yeGFP-mODC",
      degron %in% c("stable.2", "stable.3") ~ "yeGFP"
    ),
    degron = factor(degron , levels = c("yeGFP-mODC", "yeGFP-CLN2", "yeGFP"))
  ) %>%
  split(.$degron) %>% 
  map(.,function(a){
    a %>% 
      ggpairs(.,
          # legend = 5,
          columns = 1:4,
          # mapping = ggplot2::aes(color = Replicate),
          lower = list(continuous = my_fn,
                      discrete = "blank", 
                      combo="blank"), 
          # upper = "blank",
          # diag = NULL,
           diag = list(discrete="barDiag",
                      continuous = wrap("densityDiag", alpha=0.5 )),
          upper = list(discrete= wrap("barDiag" , outlier.size = 0.5),
                       combo = wrap("box_no_facet", outlier.size = 0.5),
                       continuous = wrap("cor", display_grid = FALSE, size = 2.5 , method = "pearson", color = "red4")),
          labeller = label_wrap_gen(width = 2, multi_line = TRUE))+
  theme_bw()+
  theme(text = element_text(size = 8),
        legend.position = "none",
        panel.grid.major = element_blank(),
        axis.text.x = element_text(angle = 30, hjust = 1))+
      labs(title = a$degron[1])
  })
  
gfp.area.cor.ggPair
```
```{r}
ggsave(plot = gfp.area.cor.ggPair$`yeGFP-mODC`, filename = "gfp_mODC_4Cor_ggP.png", path = "~/plots/paper1/figures/fig_2/", width = 4, height = 3.5)

ggsave(plot = gfp.area.cor.ggPair$`yeGFP-mODC`, filename = "gfp_mODC_4Cor_ggP.pdf", path = "~/plots/paper1/figures/fig_2/", width = 4, height = 3.5)

ggsave(plot = gfp.area.cor.ggPair$`yeGFP-CLN2`, filename = "gfp_cln2_4Cor_ggP.png", path = "~/plots/paper1/figures/fig_2/", width = 4, height = 3.5)

ggsave(plot = gfp.area.cor.ggPair$`yeGFP-CLN2`, filename = "gfp_cln2_4Cor_ggP.pdf", path = "~/plots/paper1/figures/fig_2/", width = 4, height = 3.5)

ggsave(plot = gfp.area.cor.ggPair$`yeGFP`, filename = "gfp_4Cor_ggP.png", path = "~/plots/paper1/figures/fig_2/", width = 4, height = 3.5)

ggsave(plot = gfp.area.cor.ggPair$`yeGFP`, filename = "gfp_4Cor_ggP.pdf", path = "~/plots/paper1/figures/fig_2/", width = 4, height = 3.5)
```

```{r}
test.ggP <- (wrap_elements(ggmatrix_gtable(gfp.area.cor.ggPair$`yeGFP-mODC`))|wrap_elements(ggmatrix_gtable(gfp.area.cor.ggPair$`yeGFP-CLN2`))| wrap_elements(ggmatrix_gtable(gfp.area.cor.ggPair$yeGFP)))+
 plot_annotation(tag_levels = "A")
```

```{r}
ggsave(plot = test.ggP, filename = "test_ggP.pdf", path = "~/plots/paper1/figures/fig_2/", width = 6.5, height = 3)
```

#area vs pup1 vs dna vs gfp
```{r}
df_corSup_fig <- mlr.df %>%
  filter(degron %in% c("mODC.2", "cln2.3", "cln2.4", "stable.2", "stable.3")) %>%
  dplyr::select(t.half,
         regressors,
         degron) %>%
  rename(
    "GFP" = "gfp.mean.bg.af.sub.new",
    "Pup1-tDimer" = "rfp.mean.bg.sub.puncta",
    "DNA" = "dapi.sum.bg.sub.puncta",
    "Area" = "area"
  ) %>%
  dplyr::select(Area, DNA, `Pup1-tDimer`,GFP,degron ) %>% 
  mutate(
    degron = case_when(
      degron %in% c("cln2.3", "cln2.4") ~ "yeGFP-CLN2",
      degron %in% c("mODC.2")  ~ "yeGFP-mODC",
      degron %in% c("stable.2", "stable.3") ~ "yeGFP"
    ),
    degron = factor(degron , levels = c("yeGFP-mODC", "yeGFP-CLN2", "yeGFP"))
  ) 
```

```{r}

area.pup1 <- df_corSup_fig %>% 
  filter(degron == "yeGFP-mODC") %>% 
  ggplot(.,aes(x = Area, y = `Pup1-tDimer`))+
  ggpointdensity::geom_pointdensity(size = 0.2, guides = F) +
      stat_cor(size = 3) +
      geom_smooth(method = "lm", se = FALSE, color = "red4", size = 0.5)+
      theme_bw() +
      labs(x = NULL) +
      theme(text = element_text(size = 8),
            axis.text.x = element_text(angle = 30),
            legend.key.size = unit(2,"mm"), 
            legend.position = "none", 
            axis.line = element_line(size = 0.1))+
  ylab("Pup1-tDimer [A.U]")+
  xlab("Area [A.U]")

dna.pup1 <- df_corSup_fig %>% 
  filter(degron == "yeGFP-mODC") %>% 
  ggplot(.,aes(x = DNA, y = `Pup1-tDimer`))+
  ggpointdensity::geom_pointdensity(size = 0.2, guides = F) +
      stat_cor(size = 3) +
      geom_smooth(method = "lm", se = FALSE, color = "red4", size = 0.5)+
      theme_bw() +
      labs(x = NULL) +
      theme(text = element_text(size = 8),
            axis.text.x = element_text(angle = 30),
            legend.key.size = unit(2,"mm"), 
            legend.position = "none", 
            axis.line = element_line(size = 0.1))+
  ylab("Pup1-tDimer [A.U]")+
  xlab("DNA [A.U]")

dna.area <- df_corSup_fig %>%
  filter(degron == "yeGFP-mODC") %>% 
  ggplot(.,aes(x = DNA, y = Area))+
  ggpointdensity::geom_pointdensity(size = 0.2, guides = F) +
      stat_cor(size = 3) +
      geom_smooth(method = "lm", se = FALSE, color = "red4", size = 0.5)+
      theme_bw() +
      labs(x = NULL) +
      theme(text = element_text(size = 8),
            axis.text.x = element_text(angle = 30),
            legend.key.size = unit(2,"mm"), 
            legend.position = "none", 
            axis.line = element_line(size = 0.1))+
  ylab("Area [A.U]")+
  xlab("DNA [A.U]")
```

```{r}
gfpInt.cor.plt <- df_corSup_fig %>% 
  pivot_longer(cols = 1:3) %>% 
  # filter(degron == "yeGFP-mODC") %>% 
  ggplot(.,aes(x = value, y = GFP))+
  ggpointdensity::geom_pointdensity(size = 0.2, guides = F) +
      stat_cor(size = 3) +
      geom_smooth(method = "lm", se = FALSE, color = "red4", size = 0.5)+
  facet_wrap(degron~name, scales = "free")+
      theme_bw() +
      labs(x = NULL) +
      theme(text = element_text(size = 8),
            axis.text.x = element_text(angle = 30),
            legend.key.size = unit(2,"mm"), 
            legend.position = "none", 
            axis.line = element_line(size = 0.1))+
  xlab("A.U")
```

```{r}
test_cor <- ((area.pup1|dna.pup1|dna.area)/gfpInt.cor.plt)+
  plot_layout(heights = c(0.2, 0.8))+
  plot_annotation(tag_levels = "A")

ggsave(plot = test_cor, filename = "area.pup1.dna.gfp_cor.pdf", path = "~/plots/paper1/figures/fig_2/", width = 6.5, height = 7)
```


#Partial correlations
ppcor
```{r}
#partial correlations
ppCor.list <- mlr.df2 %>% 
  filter(treatment == "none") %>% split(.$degron) %>% 
  map(.,function(a){
    a <- a %>% 
      dplyr::select(t.half, area,  dapi.sum.bg.sub.puncta, rfp.mean.bg.sub.puncta, gfp.mean.bg.af.sub.new) %>% 
      na.omit()
    ppcor::pcor(a)
  })

#correlations
cor.all <- mlr.df2 %>% 
  filter(treatment == "none") %>%  
  split(.$degron) %>% 
  map(.,function(a){
     a <- a %>% 
      dplyr::select(t.half, area, dapi.sum.bg.sub.puncta,rfp.mean.bg.sub.puncta, gfp.mean.bg.af.sub.new) %>% 
      na.omit()
     cor(a)
  })

```

```{r}
ppCor.df <- ppCor.list %>% 
  map(.,function(a){
    a$estimate %>% 
      as.data.frame() %>% 
      rownames_to_column(var = "regressor") %>% 
      dplyr::select(t.half, regressor) %>% 
      left_join(.,a$p.value%>% 
                  as.data.frame() %>% 
                  rownames_to_column(var = "regressor") %>% 
                  rename("p.value" = "t.half") %>% 
      dplyr::select(p.value, regressor), by = "regressor")
  }) %>% 
  bind_rows(.id = "degron")

single.core.df <- cor.all %>% 
  map(.,function(a){
    a%>% 
      as.data.frame() %>% 
      rownames_to_column(var = "regressor") %>% 
      dplyr::select(t.half, regressor)
  }) %>% 
  bind_rows(.id = "degron") %>% 
  rename("single.core"="t.half")

```



```{r}
ppCoor.fig <- ppCor.df %>% 
  left_join(.,single.core.df, by = c("degron","regressor")) %>% 
  rename("Partial-Pariwise"="t.half",
         "Standard-Pariwise" = "single.core") %>% 
  pivot_longer(cols = c("Partial-Pariwise","Standard-Pariwise")) %>% 
   mutate(regressor = case_when(regressor == "gfp.mean.bg.af.sub.new" ~ "GFP" ,
                               regressor == "area" ~ "Area",
                               regressor == "rfp.mean.bg.sub.puncta" ~ "Pup1",
                               regressor == "dapi.sum.bg.sub.puncta" ~ "DNA" ,
                               regressor == "t.half" ~ "half-life"), 
          degron = factor(degron, levels = c("yeGFP-mODC","yeGFP-CLN2","yeGFP")), 
          name = factor(name , levels = c("Standard-Pariwise", "Partial-Pariwise")), 
          sig = stars.pval(p.value), 
          sig = ifelse(sig == " ", "NS", sig)) %>% 
  # filter(p.value < 0.05) %>% 
  filter(regressor != "half-life") %>% 
  ggplot(.,aes(x = regressor , y = value, fill = name))+
  geom_col(width = 0.5, position = "dodge")+
  facet_wrap(~degron)+
  geom_hline(yintercept = 0, size = 0.1)+
  scale_y_continuous(breaks = seq(-0.5,0.5, by = 0.1))+
  theme_pubr()+
  theme(text = element_text(size = 8),
            legend.key.size = unit(4,"mm"),
        legend.position = "top", 
        axis.line = element_line(size = 0.1),
        strip.background = element_blank())+
  scale_fill_manual(values = c("lightsteelblue4" ," steelblue4"), name = NULL)+
  ylab("Correlation with Half-life ")+
  xlab("Cellular features")+
  labs(title = "C")

ppCoor.fig
```
#patchwork for 4 cellular features (area, gfp, dapi, pup1) and t-half 

```{r fig.width= 5, fig.height= 6}
gfp.4cor.hf.ppCor.plt <- (gfp.6.cor.plt/var.exp.plt/ppCoor.fig)+
  plot_layout( heights = c(0.6,0.2, 0.2))
  # plot_annotation(tag_levels = "A")

gfp.4cor.hf.ppCor.plt
```

```{r}
ggsave(plot = gfp.4cor.hf.ppCor.plt, filename = "gfp_4cor_halfLife_ppcor.png", path = "~/plots/paper1/figures/fig_2/", width = 7, height = 9)

ggsave(plot = gfp.4cor.hf.ppCor.plt, filename = "gfp_4cor_halfLife_ppcor.pdf", path = "~/plots/paper1/figures/fig_2/", width = 7, height = 9)
```

#with protein inhibion
```{r}
ppCor.PrtIn.list <- mlr.df %>% 
    filter(treatment != "none") %>%  
  filter(ifelse(treatment == "5uM", t.half < 138.6294, t.half == t.half)) %>% 
  split(.$treatment) %>% 
  map(.,function(a){
    a <- a %>% 
      dplyr::select(t.half, area, dapi.sum.bg.sub.puncta, rfp.mean.bg.sub.puncta, gfp.mean.bg.af.sub.new) %>%
      na.omit()
    ppcor::pcor(a)
  })

cor.PrtIn.all <- mlr.df %>% 
    filter(treatment != "none") %>%  
  filter(ifelse(treatment == "5uM", t.half < 138.6294, t.half == t.half)) %>% 
  split(.$treatment) %>% 
  map(.,function(a){
    a <- a %>% 
      dplyr::select(t.half, area, dapi.sum.bg.sub.puncta, rfp.mean.bg.sub.puncta, gfp.mean.bg.af.sub.new) %>% 
      na.omit()
    cor(a)
  })

```

```{r}
ppCor.PrtIn.df <- ppCor.PrtIn.list %>% 
  map(.,function(a){
    a$estimate %>% 
      as.data.frame() %>% 
      rownames_to_column(var = "regressor") %>% 
      dplyr::select(t.half, regressor) %>% 
      left_join(.,a$p.value%>% 
                  as.data.frame() %>% 
                  rownames_to_column(var = "regressor") %>% 
                  rename("p.value" = "t.half") %>% 
      dplyr::select(p.value, regressor), by = "regressor")
  }) %>% 
  bind_rows(.id = "treatment")

single.core.Prtn.df <- cor.PrtIn.all %>% 
  map(.,function(a){
    a%>% 
      as.data.frame() %>% 
      rownames_to_column(var = "regressor") %>% 
      dplyr::select(t.half, regressor)
  }) %>% 
  bind_rows(.id = "treatment") %>% 
  rename("single.core"="t.half")
```

correlation of pup1 with mODC with treated with proteasome inhibitors
```{r}
ppcor.fig.df <- ppCor.PrtIn.df %>% 
  left_join(.,single.core.Prtn.df, by = c("treatment","regressor")) %>% 
  filter(!(treatment %in% c("dmso1","50uM"))) %>% 
  rename("Partial-Pariwise"="t.half",
         "Standard-Pariwise" = "single.core") %>% 
  pivot_longer(cols = c("Partial-Pariwise","Standard-Pariwise")) %>% 
   mutate(regressor = case_when(regressor == "gfp.mean.bg.af.sub.new" ~ "GFP" ,
                               regressor == "area" ~ "Area",
                               regressor == "rfp.mean.bg.sub.puncta" ~ "Pup1",
                               regressor == "dapi.sum.bg.sub.puncta" ~ "DNA" ,
                               regressor == "t.half" ~ "half-life", 
                               TRUE ~ regressor), 
          treatment = factor(treatment, levels = c("dmso1","50uM","dmso2","1uM","2.5uM","5uM")), 
          name = factor(name , levels = c("Standard-Pariwise", "Partial-Pariwise")), 
          sig = stars.pval(p.value), 
          sig = ifelse(sig == "\ ", "N.S", sig))
          # new.value = ifelse((name == "Partial-Pariwise" & p.value > 0.05), 0, value)) %>% 
  # filter(p.value < 0.05) %>% 
ppcor.fig.df %>% 
  filter(!(regressor %in% c("half-life", "shape")), 
         regressor == "Pup1", 
         treatment %in% c("dmso2","1uM","2.5uM","5uM")) %>% 
  ggplot(.,aes(x = treatment , y =value, fill = name))+
  geom_col(width = 0.5, position = "dodge")+
  geom_text(data = ppcor.fig.df %>% 
              filter(!(regressor %in% c("half-life", "shape")), 
         regressor == "Pup1", 
         treatment %in% c("dmso2","1uM","2.5uM","5uM")) , 
         aes(label = sig), nudge_x = -0.1)+
  # facet_wrap(~treatment, nrow = 1)+
  scale_y_continuous(breaks = seq(-0.5,0.5, by = 0.1))+
  theme_pubr()+
  theme(text = element_text(size = 8),
            legend.key.size = unit(4,"mm"),
        legend.position = "top")+
  scale_fill_manual(values = c("lightsteelblue4" ," steelblue4"), name = NULL)+
  geom_hline(yintercept = 0)+
  ylab("Correlation of Pup1 with Half-life ")+
  xlab("Protein inhibitor treatment (MG132)")
```

#when you normalize pup1 with the area
```{r}
mlr.df %>%
  mutate(dm = log10(dm)) %>% 
  filter(degron %in% c("mODC.2", "cln2.3", "cln2.4", "stable.2", "stable.3")) %>%
  dplyr::select(t.half,
         regressors,
         degron) %>%
  rename(
    "GFP" = "gfp.mean.bg.af.sub.new",
    "Pup1-tDimer" = "rfp.mean.bg.sub.puncta",
    "DNA" = "dapi.sum.bg.sub.puncta",
    "Area" = "area"
  ) %>%
  mutate(norm.pup1 = `Pup1-tDimer`/Area) %>% 
  pivot_longer(cols = c(2:7,9)) %>%
  mutate(degron = case_when(
      degron %in% c("cln2.3", "cln2.2", "cln2", "cln2.4") ~ "yeGFP-CLN2",
      degron %in% c("mODC.2", "mODC")  ~ "yeGFP-mODC",
      degron %in% c("stable", "stable.2", "stable.3") ~ "yeGFP"
    ),
    degron = factor(degron , levels = c("yeGFP-mODC", "yeGFP-CLN2", "yeGFP"))
  ) %>%
  filter(!(name %in% c("shape","dm"))) %>% 
  ggplot(., aes(x = value, y = t.half)) +
      ggpointdensity::geom_pointdensity(size = 0.2, guides = F) +
      stat_cor(size = 2) +
      geom_smooth(method = "lm", se = FALSE, color = "red4", size = 0.5)+
      facet_wrap(degron ~ name, scales = "free", ncol = 4) +
      theme_bw() +
      labs(x = NULL) +
      theme(text = element_text(size = 8),
            axis.text.x = element_text(angle = 30),
            legend.key.size = unit(2,"mm"), 
            legend.position = "none")+
  ylab("Half-Life [min.]")+
  xlab("A.U")
```

